Many of us enter tech with years of experience in another field, bringing valuable skills to the pivot. But that transition is rarely smooth or easy. Speaking to other founders who have made a tech pivot has been tremendously valuable.
I come from the entertainment and dating space, mostly working B2C as a service provider. The shift to a B2B SaaS start-up meant changing my approach to business development and adapting my marketing know-how to a very different audience and new clientele (brands/employers). The hardest part is realizing that some of my skills aren t transferable 1:1, so I have to adapt my B2C go-to strategies for B2B. It s a different animal.
For example, in B2C, articulating the problem and helping your customer see themselves in your solution requires a different skill. It s about meeting your customer emotionally and connecting with their desired outcome, which is very different from B2B. With B2B, you re building longer-term relationships and discussing ROI. It s less emotional and more about efficiency. The pain points are different, but in many respects the conversation is the same. I can help you, and here s how.
What learnings, challenges, or obstacles have you had to overcome to succeed in tech? What advice do you have for newcomers launching a product or service in this space?
Would you use this? creates optimistic answers. What did you do the last time this happened? gives you a workflow, cost, workaround, and competing priority.
The follow-up I like is: what did you do instead when the product was unavailable, confusing, or not worth paying for?
That answer reveals the real competitor. Sometimes it is another tool. Often it is a spreadsheet, a friend, a search tab, or doing nothing. A beta is more useful when it reconstructs behavior than when it collects feature wishes.
What substitution surprised you most in user research?
A model can produce an answer quickly while moving the real work into checking it.
For any benchmark meant to represent useful work, I want two numbers: time to first answer and time until a human can accept the result with confidence. The gap includes source checking, reruns, manual tests, cleanup, and reversals.
A system that is 30 percent faster at generation but doubles verification cost is not more productive. It has just moved latency out of the headline metric.
What would you include in a practical verification-cost benchmark?
Launch retrospectives often become traffic summaries: rank, views, signups, comments. Useful, but none of those says what the team learned.
I think every postmortem should name one belief the launch disproved. Maybe the audience misunderstood the promise, setup friction mattered more than price, or the users who converted were not the segment you expected.
If nothing could have changed your mind, the launch was distribution, not an experiment.
What belief did your last launch force you to update?
Automation demos end at the click. Real operations begin when the click partially succeeds.
If an agent creates a record, sends a request, updates permissions, or starts a job and then loses context, who owns the next step? A human needs an exact action log, current external state, safe retry behavior, and a clear answer to whether repeating the action will duplicate anything.
Agent failed is not a handoff.
What information would you need before taking over a failed automated task?
Retention dashboards usually tell us who came back. They rarely tell us whether the product helped someone return after falling behind.
For learning, fitness, finance, and any habit-shaped product, recovery deserves its own measurement: time from interruption to meaningful return, the smallest successful comeback action, and whether the product reduces guilt or adds to it.
A system that only works for perfect streaks is optimized for the users who need it least.
What does a good recovery path look like in your product?
Personalization gets dangerous when the system treats its inference as more authoritative than the person using it.
If a user says this is too easy, changes a goal, removes a topic, or asks the system to forget a preference, that correction should take effect immediately. It should not become one more weak signal waiting to be averaged against weeks of behavior.
We often cover software topics on ProductHunt, but sometimes we overlook the fact that tech can be felt most in the physical world (hardware).
As of mid-2024, there have been approximately 3,979 reported incidents involving autonomous vehicles (AVs) in the United States since reporting began in 2019. Source
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